o
    zio                     @   sV   d dl Z d dlmZ ddlmZmZmZ de jdede jfdd	Z	G d
d deZ
dS )    N)	Optimizer   )OptFloatOptLossClosureParamsmatrixpowerreturnc                 C   s>   | j }|  } t| \}}}|||  |  |S N)devicecputorchsvdpow_diagtto)r   r   r   usv r   K/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/shampoo.py_matrix_power   s    r   c                       sZ   e Zd ZdZ					ddededed	ed
edef fddZddede	fddZ
  ZS )Shampooa  Implements Shampoo Optimizer Algorithm.

    It has been proposed in `Shampoo: Preconditioned Stochastic Tensor
    Optimization`__.

    Arguments:
        params: iterable of parameters to optimize or dicts defining
            parameter groups
        lr: learning rate (default: 1e-3)
        momentum: momentum factor (default: 0)
        weight_decay: weight decay (L2 penalty) (default: 0)
        epsilon: epsilon added to each mat_gbar_j for numerical stability
            (default: 1e-4)
        update_freq: update frequency to compute inverse (default: 1)

    Example:
        >>> import torch_optimizer as optim
        >>> optimizer = optim.Shampoo(model.parameters(), lr=0.01)
        >>> optimizer.zero_grad()
        >>> loss_fn(model(input), target).backward()
        >>> optimizer.step()

    __ https://arxiv.org/abs/1802.09568

    Note:
        Reference code: https://github.com/moskomule/shampoo.pytorch
    皙?        -C6?r   paramslrmomentumweight_decayepsilonupdate_freqc                    s   |dkrt d||dk rt d||dk r!t d||dk r,t d||dk r7t d|t|||||d}tt| || d S )Nr   zInvalid learning rate: {}zInvalid momentum value: {}zInvalid weight_decay value: {}r   )r   r   r    r!   r"   )
ValueErrorformatdictsuperr   __init__)selfr   r   r   r    r!   r"   defaults	__class__r   r   r'   ,   s(   
zShampoo.__init__Nclosurer	   c              
   C   s&  d}|dur	| }| j D ]}|d D ]}|jdu rq|jj}| }| }| j| }|d }	|d }
t|dkrud|d< |	dkrH| |d< t| D ]&\}}|d t	j
||||d	 |d
|< ||| |dj|d< qN|	dkr|d|	 j|d |	d |
dkr|j|j|d d t| D ]\\}}|d
| }|d| }|d| }| }||d}| }|||  |d |d  dkr|t|d|  ||d kr|| }||}q|| }||}q|d  d7  < ||d< |jj||d  d qq|S )zPerforms a single optimization step.

        Arguments:
            closure: A closure that reevaluates the model and returns the loss.
        Nr   r   r    r   stepmomentum_bufferr!   )outz
precond_{}zinv_precond_{dim_id})dim_idr   )alphazinv_precond_{}r"   r   )param_groupsgraddata
ndimensionsizestatelenclone	enumerater   eyenewr$   zero_mul_add_
transpose_
contiguousviewr   copy_r   )r(   r,   lossgrouppr4   orderoriginal_sizer8   r   r    r0   dimprecondinv_precondtransposed_sizegrad_tr   r   r   r-   L   sj   


<zShampoo.step)r   r   r   r   r   r
   )__name__
__module____qualname____doc__r   floatintr'   r   r   r-   __classcell__r   r   r*   r   r      s*     r   )r   torch.optim.optimizerr   typesr   r   r   TensorrS   r   r   r   r   r   r   <module>   s
    